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Did you ever want to do a quick exploratory pass on a panel data set? Did you ever wish to give somebody (e.g., a reviewer or a fellow researcher), the opportunity to explore your data and your findings but can’t provide your raw data? Do you get bored from producing the same tables and figures over and over again for your panel data project? If your answer to one of the questions above is yes, then the new ExPanDaR R package might be worth a look.

ExPanDaR does two things:

1. It provides the code base for the ExPanD web app. ExPanD is a shiny based app supporting interactive exploratory data analysis. See here for an online version presenting the same data that we will explore below.

2. It provides a set of functions that I hope is useful for a panel data exploration workflow and prepares output that you would include into a typical applied panel data study.

To see what ExPanDaR has to offer, let’s take a quick tour. For more detailed guidance on how to use a specific function presented below, take a look at the package documentation.

## Data Preparation

ExPanDaR expects the data to be organized in long format. This implies that each observation (a row) is identified by cross-sectional and time series identifiers and that variables are organized by columns. While you can have a vector of variables jointly determining the cross-section, the time-series needs to be identified by a unique sortable variable. The ExPanDaR functions treat cross-sectional identifiers as factors and expect the time-series identifier to be coercible into an ordered factor.

For this walk-through I will use the data set russell_3000, which comes with the package. It contains some financial reporting and stock return data of Russell 3000 firms and has been collected from Google Finance and Yahoo Finance using the tidyquant package in the summer of 2017. A word of caution: While the data appears to be relatively decent quality I would advise against using this data for scientific work without verifying its integrity first. These are the variables included in the data.

Variable Definition
coid Company identifier
period Fiscal year
coname Company name
sector Sector
industry Industry
toas Total assets at period end (M US-$) sales Sales of the period (M US-$)
equity Total equity at period end (M US-$) debt_ta Total debt (% total assets) eq_ta Total equity (% total assets) gw_ta Goodwill (% total assets) oint_ta Other intangible assets (% total assets) ppe_ta Property, plant and equipment (% total assets) ca_ta Current assets (% total assets) cash_ta Cash (% total assets) roe Return on equity (net income divided by average equity) roa Return on assets (earnings before interest and taxes divided by average equity) nioa Net income divided by average assets cfoa Cash flow from operations divided by average assets accoa Total accruals divided by average assets cogs_sales Cost of goods sold divided by sales ebit_sales Earnings before interest and taxes divided by sales ni_sales Net income divided by sales return Stock return of the period (%) Let’s see whether coid and period identify a panel observation. any(duplicated(russell_3000[,c("coid", "period")])) ## [1] FALSE This seems to be the case. Now you can start exploring the data interactively. The code ExPanD(df = russell_3000, df_def = russell_3000_data_def, config_list = ExPanD_config_russell_3000, abstract = "Data is from Google Finance and Yahoo Finance.") will start the ExPanD app (online instance can be found here). But in this blog post I want to showcase the functions of the ExPanDaR package. So, as a first step, let’s use ExPanDaR’s function prepare_missing_values_graph() to eyeball how frequently observations are missing in the data set. prepare_missing_values_graph(russell_3000, ts_id = "period") OK. This does not look too bad. Only FY2013 seems odd, as some variables are completely missing. Guess why? They are calculated using lagged values of total assets. So, in the following, let’s focus on some profitability measures and on the fiscal years 2014 to 2016 (a short panel, I know). Time to check the descriptive statistics using the prepare_descriptive_table() function. r3 <- droplevels(russell_3000[russell_3000$period > "FY2013",
c("coid", "coname", "period", "sector", "toas",
"nioa", "cfoa", "accoa", "return")])
t <- prepare_descriptive_table(r3)
t$kable_ret %>% kable_styling("condensed", full_width = F, position = "center") (#tab:descriptive_statistics_table)Descriptive Statistics N Mean Std. dev. Min. 25 % Median 75 % Max. toas 6,643 8,722.510 35,846.713 0.800 463.225 1,600.050 5,100.500 861,395.000 nioa 6,399 0.001 0.096 -2.692 -0.002 0.017 0.037 0.463 cfoa 6,399 0.033 0.084 -2.121 0.021 0.041 0.065 0.460 accoa 6,399 -0.032 0.052 -0.712 -0.046 -0.026 -0.012 0.621 return 6,009 0.097 0.433 -0.938 -0.136 0.065 0.269 6.346 Take a look at the minima and the maxima of some of the variables (e.g., net income over assets (nioa)). Normally, it should be around -50 % to + 50%. Our measure has a minimum way below -50 %. One thing that comes very handy when dealing with outliers is a quick way to observe extreme values. prepare_ext_obs_table() might be helpful here. t <- prepare_ext_obs_table(r3, cs_id = "coname", ts_id = "period", var = "nioa") t$kable_ret %>%
kable_styling("condensed", full_width = F, position = "center")
coname period nioa
Gamco Investors, Inc. FY2016 0.463
Gaia, Inc. FY2016 0.369
Five Prime Therapeutics, Inc. FY2015 0.355
Ligand Pharmaceuticals Incorporated FY2015 0.302
Proteostasis Therapeutics, Inc. FY2015 -0.822
Proteostasis Therapeutics, Inc. FY2014 -0.830
Omeros Corporation FY2015 -1.255
vTv Therapeutics Inc. FY2014 -1.269
Omeros Corporation FY2014 -2.692

In a real life research situation, you might want to take a break and check your data as well as the actual financial statements to see what is going on. In most cases, you will see that the outliers are caused by very small denominators (average total assets in this case). To reduce the effect of these outliers on your analysis, you can winsorize (or truncate) them by using the treat_outliers() function.

r3win <- treat_outliers(r3, percentile = 0.01)
t <- prepare_ext_obs_table(r3win, cs_id = "coname", ts_id = "period", var = "nioa")
t$kable_ret %>% kable_styling("condensed", full_width = F, position = "center") coname period nioa ABIOMED, Inc. FY2015 0.147 Acacia Communications, Inc. FY2015 0.147 Acacia Communications, Inc. FY2016 0.147 Aspen Technology, Inc. FY2015 0.147 Aspen Technology, Inc. FY2016 0.147 Workhorse Group, Inc. FY2015 -0.355 Workhorse Group, Inc. FY2016 -0.355 EXCO Resources NL FY2015 -0.355 ZIOPHARM Oncology Inc FY2015 -0.355 ZIOPHARM Oncology Inc FY2016 -0.355 ## Descriptive Statistics This looks better. Let’s look at the winsorized descriptive statistics. t <- prepare_descriptive_table(r3win) t$kable_ret  %>%
kable_styling("condensed", full_width = F, position = "center")
(#tab:descriptive_statistics_table_winsorized)Descriptive Statistics
N Mean Std. dev. Min. 25 % Median 75 % Max.
toas 6,643 7,198.735 17,632.076 45.400 463.225 1,600.050 5,100.500 122,418.180
nioa 6,399 0.002 0.077 -0.355 -0.002 0.017 0.037 0.147
cfoa 6,399 0.034 0.068 -0.280 0.021 0.041 0.065 0.178
accoa 6,399 -0.032 0.043 -0.209 -0.046 -0.026 -0.012 0.092
return 6,009 0.088 0.375 -0.707 -0.136 0.065 0.269 1.568

Ok. Most firms have positive cash flows from operations and positive net income. Accruals (the difference between net income and cash flow from operations) are mostly negative. How does firms’ net income vary across sectors?

ret <- prepare_by_group_bar_graph(r3win, by_var = "sector", var = "nioa",
stat_fun = mean, order_by_stat = TRUE)
## Warning: package 'bindrcpp' was built under R version 3.4.4
ret$plot Whoa. This is not looking too good for the health sector. Let’s take a quick look at the distribution. ret <- prepare_by_group_violin_graph(r3win, by_var = "sector", var = "nioa", order_by_mean = TRUE) ret You can see that there is a relatively large mass of loss making firms in the health sector. This is not too surprising given that many publicly-listed health firms are relatively small growth firms. Also, you can see the effect of regulated profits in the public utilities sector. ## Correlation Analysis I am sure that you won’t care but I am a big fan of correlation tables. prepare_correlation_table() prepares a table reporting Pearson correlations above and Spearman correlations below the diagonal. t<- prepare_correlation_table(r3win, bold = 0.01, format="html") t$kable_ret %>%
kable_styling("condensed", full_width = F, position = "center")
A B C D E
A: toas 0.10 0.07 0.08 -0.00
B: nioa 0.17 0.80 0.49 0.15
C: cfoa 0.10 0.66 -0.09 0.12
D: accoa 0.20 0.38 -0.30 0.07
E: return 0.04 0.20 0.14 0.08
This table reports Pearson correlations above and Spearman correlations below the diagonal. The number of observations ranges from 6002 to 6643. Correlations with significance levels below 1% appear in bold print.

In fact, I like correlations so much that especially for samples containing many variables I use prepare_correlation_graph() to display a graphic variant based on the corrplot package. See for yourself.

ret <- prepare_correlation_graph(r3win)

## Scatter Plots

prepare_scatter_plot() produces the mother of all plots, the scatter plot. Here we take a look at the association of net income and stock returns.

prepare_scatter_plot(r3win, x="nioa", y="return", color="sector", size="toas", loess = 1)

They are mildly positively associated indicating that more profitable firms also tend to have higher stock returns. Do you see the structural break around nioa == 0? This indicates that firms and/or the stock market treats losses differently than gains. Researchers in the area of accounting tend to find that interesting.

## Regression Tables

Finally, if you happen to be a fan of starred numbers, you can also quickly produce regression tables by using the function prepare_regression_table() that calls lfe::felm() for OLS and glm() for binary logit models. The tables are then constructed by calling stargazer::stargazer(), allowing for plain text, html and latex output.

You can construct tables by mixing different models…

dvs <- c("return", "return", "return", "return", "return", "return")
idvs <- list(c("nioa"),
c("cfoa"),
c("accoa"),
c("cfoa", "accoa"),
c("nioa", "accoa"),
c("nioa", "accoa"))
feffects <- list("period", "period", "period",
c("period", "coid"), c("period", "coid"), c("period", "coid"))
clusters <- list("", "", "", c("coid"), c("coid"), c("period", "coid"))
t <- prepare_regression_table(r3win, dvs, idvs, feffects, clusters)
htmltools::HTML(t$table)  Dependent variable: return return return return return return (1) (2) (3) (4) (5) (6) nioa 0.772*** 1.794*** 1.794*** (0.064) (0.384) (0.511) cfoa 0.705*** 1.582*** (0.073) (0.410) accoa 0.534*** 0.876** -0.890** -0.890 (0.116) (0.381) (0.395) (0.553) Estimator ols ols ols ols ols ols Fixed effects period period period period, coid period, coid period, coid Std. errors clustered No No No coid coid period, coid Observations 6,002 6,002 6,002 6,002 6,002 6,002 R2 0.054 0.047 0.035 0.341 0.344 0.344 Adjusted R2 0.054 0.046 0.035 -0.044 -0.039 -0.039 Note: *p<0.1; **p<0.05; ***p<0.01 … or by applying one model on different sub-samples. t <- prepare_regression_table(r3win, "return", c("nioa", "accoa"), byvar="period") htmltools::HTML(t$table)
 Dependent variable: return Full Sample FY2014 FY2015 FY2016 (1) (2) (3) (4) nioa 0.801*** 0.234* 0.607*** 1.375*** (0.074) (0.139) (0.123) (0.119) accoa -0.067 0.014 0.638*** -1.224*** (0.132) (0.240) (0.205) (0.233) Constant 0.082*** 0.130*** 0.014 0.096*** (0.006) (0.011) (0.011) (0.011) Estimator ols ols ols ols Fixed effects None None None None Std. errors clustered No No No No Observations 6,002 1,817 2,033 2,152 R2 0.023 0.002 0.033 0.058 Adjusted R2 0.023 0.001 0.032 0.057 Note: *p<0.1; **p<0.05; ***p<0.01

## Conclusion

This is all there is (currently). All these functions are rather simple wrappers around established R functions. They can easily be modified to fit your needs and taste. Take look at the github repository of the ExPanDaR package for the code and do not hesitate to touch base for feedback. Enjoy!